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OLSengine: Transparent Linear and Causal Inference Models 🚀

OLSengine is an open-source R package designed for applied researchers in social sciences. It provides a comprehensive, zero-dependency mathematical engine for fundamental statistical methods and modern causal inference techniques.

Built under the philosophy of “Assisted Simplicity”, OLSengine acts as a methodological customs filter (“Aduana”). It unifies model estimation and diagnostics in a single step, alerting researchers to violations of mathematical assumptions and guiding them toward robust alternatives without making automatic decisions behind their backs.

🌟 Core Features

• Six Estimation Engines: OLS regression, ANOVA/t-tests, logistic regression, panel data (FE/RE), instrumental variables (2SLS), and difference-in-differences (DiD).
• Zero External Dependencies: Built entirely on pure Base R matrix algebra and native stats functions for maximum long-term stability and algorithmic transparency.
• The Methodological Customs (Aduana): Automatically runs background diagnostics (Breusch-Pagan, Shapiro-Wilk/Lilliefors, Levene, VIF, Hosmer-Lemeshow, Hausman, first-stage F for weak instruments, a descriptive pre-treatment level comparison for DiD) and outputs actionable, literature-backed warnings.
• Paper-Ready Outputs: Returns hierarchical tables formatted for direct inclusion in academic manuscripts (APA style), automatically calculating effect sizes and exact p-values.
• Publication-Ready Plots: Generates APA-style, grayscale plots (forest plots, group means, logistic curves, panel trends, DiD group means with counterfactual) without requiring ggplot2.

📦 Installation

Install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("msoto-perez/OLSengine")

🛠️ Quick Start

The package revolves around a single, powerful wrapper function: paper_engine().

1. OLS Regression (with Robust SE)

Detects heteroskedasticity and multicollinearity. HC3 robust standard errors available.

library(OLSengine)

# Standard OLS
model_ols <- paper_engine(y ~ x1 + x2, data = my_data, model = "ols")

# With HC3 Robust Standard Errors
model_robust <- paper_engine(y ~ x1 + x2, data = my_data, model = "ols", robust = TRUE)

# View results
model_robust$tables$Table2_OLS_Estimation
model_robust$messages

# Generate forest plot
plot_engine(model_robust)

2. ANOVA / t-tests (4-Way Engine)

Handles independent or paired designs, parametric and non-parametric tests.

# Auto-pilot: if residual normality is rejected, Levene's test (Brown-Forsythe)
# routes to Welch's ANOVA (p <= .10) or Kruskal-Wallis (p > .10)
model_anova <- paper_engine(score ~ group, data = experiment_data, 
                            model = "anova", non_parametric = "auto")

# Generate group means plot with 95% CI
plot_engine(model_anova)

3. Logistic Regression

Reports Odds Ratios, McFadden’s and Nagelkerke’s Pseudo R², classification accuracy.

model_logit <- paper_engine(purchased ~ age + income, data = consumer_data, 
                            model = "logit")

# Generate predicted probability curve
plot_engine(model_logit)

4. Panel Data (Fixed/Random Effects)

Hausman test automatically selects between fixed and random effects.

model_panel <- paper_engine(wage ~ experience + education, 
                            data = panel_data, 
                            model = "panel",
                            entity_id = "worker_id",
                            time_id = "year",
                            method = "auto")  # Hausman test decides

plot_engine(model_panel)

5. Instrumental Variables (2SLS)

Reports the conventional first-stage F statistic, which assumes homoskedastic errors, with a rule-of-thumb weak-instrument warning at F < 10 (Staiger & Stock, 1997); F above 10 does not rule out weak identification. Overidentification is tested with the Sargan test, which also assumes homoskedasticity. Every right-hand-side variable is treated as endogenous: included exogenous control variables are not supported.

model_iv <- paper_engine(education ~ income, 
                         data = wage_data,
                         model = "iv",
                         instruments = ~ father_education + region)

# Diagnostics include first-stage F-stat
model_iv$messages
plot_engine(model_iv)

6. Difference-in-Differences

Estimates the two-group, two-period DiD effect and reports a descriptive comparison of pre-treatment group means. With a single pre-treatment period this is not a test of parallel trends, which requires multiple pre-treatment periods.

model_did <- paper_engine(outcome ~ 1, 
                          data = policy_data,
                          model = "did",
                          treatment_var = "treated",
                          time_var = "period",
                          treatment_level = "Treated",
                          post_level = "Post")

# Plot shows pre/post levels, the counterfactual, and the treatment effect
plot_engine(model_did)

📊 Example Dataset

The package includes academic_salaries, a real dataset of 397 U.S. college professors:

data(academic_salaries)

# Explore salary determinants
salary_model <- paper_engine(salary ~ rank + discipline + years_since_phd + sex,
                             data = academic_salaries,
                             model = "ols",
                             robust = "auto")

🔬 Numerical Validation

All engines have been validated against standard R packages (lm, aov, glm, plm, ivreg) with numerical precision < 0.001. See validation.R for complete verification.

📖 Citation

To cite OLSengine in publications:

Soto-Pérez, M. (2026). OLSengine: A transparent and assisted econometric 
modelling engine in base R (v1.2.1). R package. 
https://github.com/msoto-perez/OLSengine

🤝 Contributing

Issues and pull requests are welcome at: https://github.com/msoto-perez/OLSengine/issues

📄 License

MIT License - see LICENSE file for details.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.